Artykuły udostępnione publicznie: - Kiran Koshy ThekumparampilWięcej informacji
Dostępne w jakimś miejscu: 9
Robustness of conditional gans to noisy labels
KK Thekumparampil, A Khetan, Z Lin, S Oh
Advances in neural information processing systems 31, 2018
Upoważnienia: US National Science Foundation
Efficient algorithms for smooth minimax optimization
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in neural information processing systems 32, 2019
Upoważnienia: US National Science Foundation
Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans
Z Lin, K Thekumparampil, G Fanti, S Oh
international conference on machine learning, 6127-6139, 2020
Upoważnienia: US National Science Foundation
Learning from comparisons and choices
S Negahban, S Oh, KK Thekumparampil, J Xu
Journal of Machine Learning Research 19 (40), 1-95, 2018
Upoważnienia: US National Science Foundation
Lifted primal-dual method for bilinearly coupled smooth minimax optimization
KK Thekumparampil, N He, S Oh
International conference on artificial intelligence and statistics, 4281-4308, 2022
Upoważnienia: US National Science Foundation
Projection efficient subgradient method and optimal nonsmooth frank-wolfe method
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in neural information processing systems 33, 12211-12224, 2020
Upoważnienia: US National Science Foundation
Dpzero: Private fine-tuning of language models without backpropagation
L Zhang, B Li, KK Thekumparampil, S Oh, N He
arXiv preprint arXiv:2310.09639, 2023
Upoważnienia: US National Science Foundation, Swiss National Science Foundation
Bring your own algorithm for optimal differentially private stochastic minimax optimization
L Zhang, KK Thekumparampil, S Oh, N He
Advances in Neural Information Processing Systems 35, 35174-35187, 2022
Upoważnienia: US National Science Foundation, Swiss National Science Foundation
Statistically and computationally efficient linear meta-representation learning
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in Neural Information Processing Systems 34, 18487-18500, 2021
Upoważnienia: US National Science Foundation
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